A secured storage and privacy-preserving model using CRT for providing security on cloud and IoT-based applications
Cloud computing and Internet of Things (IoT) are emerging technologies which have the capability to enhance the human daily lifestyle. Moreover, the combination of Cloud computing and IoT are increasing the productivity of the enormous number of applications in various fields like supply chains, com...
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Veröffentlicht in: | Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2019-03, Vol.151, p.181-190 |
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Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Cloud computing and Internet of Things (IoT) are emerging technologies which have the capability to enhance the human daily lifestyle. Moreover, the combination of Cloud computing and IoT are increasing the productivity of the enormous number of applications in various fields like supply chains, commercial, engineering, manufacturing etc. Presently, security is a very big threat in cloud computing and the Internet of things areas. For that purpose, the various researchers have introduced many privacy-preserving models and security mechanisms in recent years. Even though, they have failed to achieve the latest cloud user's expectation in terms of security level. For satisfying the cloud users, this paper proposes a new Chinese Remainder Theorem (CRT)-based data storage mechanism for storing the user data securely in cloud database. Moreover, a new group key management scheme is also built using CRT to access the encrypted data from the cloud database. In the proposed CRT-based secured storage scheme adopts two encryption schemes which use new formulas for performing the first and second encryption and also introduced a new formula for decrypting the cloud data. In addition, a new formula is introduced in the process of group key generation for accessing the encrypted cloud data from the cloud database in a cloud server. The performance level of the security models has been evaluated by analyzing the experimental results. Finally, it is proven that the proposed data security model is better than other existing models. |
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ISSN: | 1389-1286 1872-7069 |
DOI: | 10.1016/j.comnet.2019.01.032 |